Lab 3 - The Recipes package

SOLUTIONS

Packages

We will use the following package in this lab.

# check if 'librarian' is installed and if not, install it
if (! "librarian" %in% rownames(installed.packages()) ){
  install.packages("librarian")
}
  
# load packages if not already loaded
librarian::shelf(
  tidyverse, magrittr, gt, gtExtras, tidymodels, DataExplorer, skimr, janitor, ggplot2
)

Warning: package 'gt' was built under R version 4.3.3

Warning: package 'modeldata' was built under R version 4.3.3

# library(magrittr)   # the pipe
# library(tidyverse)  # for data wrangling + visualization
# library(tidymodels) # for modeling
# library(gt)         # for pretty tables

theme_set(theme_bw(base_size = 12))
boston_cocktails <- readr::read_csv('../data/boston_cocktails.csv', show_col_types = FALSE)

Data: The Boston Cocktail Recipes

The Boston Cocktail Recipes dataset appeared in a TidyTuesday posting. TidyTuesday is a weekly data project in R.

The dataset is derived from the Mr. Boston Bartender’s Guide, together with a dataset that was web-scraped as part of a hackathon.

This dataset contains the following information for each cocktail:

variable	class	description
name	character	Name of cocktail
category	character	Category of cocktail
row_id	integer	Drink identifier
ingredient_number	integer	Ingredient number
ingredient	character	Ingredient
measure	character	Measurement/volume of ingredient
measure_number	real	measure as a number

Exercises

Exercise 1

First use skimr::skim and DataExplorer::introduce to assess the quality of the data set.

Next prepare a summary. What is the median measure number across cocktail recipes?

SOLUTION:

boston_cocktails %>% skimr::skim()

Data summary

Name	Piped data
Number of rows	2542
Number of columns	7
_______________________
Column type frequency:
character	4
numeric	3
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
name	0	1	4	36	0	937	0
category	0	1	3	21	0	11	0
ingredient	0	1	3	23	0	40	0
measure	0	1	1	8	0	28	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
row_id	0	1	495.37	284.75	1.00	261.25	497	730.75	990	▇▇▇▇▇
ingredient_number	0	1	2.54	1.29	1.00	1.00	2	3.00	6	▇▃▂▁▁
measure_number	0	1	0.98	0.72	0.02	0.50	1	1.50	16	▇▁▁▁▁

boston_cocktails %>% DataExplorer::introduce()

# A tibble: 1 × 9
   rows columns discrete_columns continuous_columns all_missing_columns
  <int>   <int>            <int>              <int>               <int>
1  2542       7                4                  3                   0
# ℹ 4 more variables: total_missing_values <int>, complete_rows <int>,
#   total_observations <int>, memory_usage <dbl>

boston_cocktails %>% summary()

     name             category             row_id      ingredient_number
 Length:2542        Length:2542        Min.   :  1.0   Min.   :1.000    
 Class :character   Class :character   1st Qu.:261.2   1st Qu.:1.000    
 Mode  :character   Mode  :character   Median :497.0   Median :2.000    
                                       Mean   :495.4   Mean   :2.545    
                                       3rd Qu.:730.8   3rd Qu.:3.000    
                                       Max.   :990.0   Max.   :6.000    
  ingredient          measure          measure_number   
 Length:2542        Length:2542        Min.   : 0.0200  
 Class :character   Class :character   1st Qu.: 0.5000  
 Mode  :character   Mode  :character   Median : 1.0000  
                                       Mean   : 0.9797  
                                       3rd Qu.: 1.5000  
                                       Max.   :16.0000  

The median measure is 1.0. Note that the dimensions are identified as ounces (oz) in the measure column.

Exercise 2

From the boston_cocktails dataset select the name, category, ingredient, and measure_number columns and then pivot the table to create a column for each ingredient. Fill any missing values with the number zero.

Since the names of the new columns may contain spaces, clean them using the janitor::clean_names(). Finally drop any rows with NA values and save this new dataset in a variable.

How much gin is in the cocktail called Leap Frog Highball?

SOLUTION:

cocktails_df <- boston_cocktails %>%
  # select the columns (by de-selecting the ones we don't want)
  dplyr::select(-ingredient_number, -row_id, -measure) %>%
  # pivot wider (make more columns); use zeros in place of NA values
  tidyr::pivot_wider(
    names_from = ingredient
    , values_from = measure_number
    , values_fill = 0
  ) %>%
  janitor::clean_names() %>%
  tidyr::drop_na()
# show the table in the document
cocktails_df

# A tibble: 937 × 42
   name    category light_rum lemon_juice lime_juice sweet_vermouth orange_juice
   <chr>   <chr>        <dbl>       <dbl>      <dbl>          <dbl>        <dbl>
 1 Gauguin Cocktai…      2           1          1               0           0   
 2 Fort L… Cocktai…      1.5         0          0.25            0.5         0.25
 3 Cuban … Cocktai…      2           0          0.5             0           0   
 4 Cool C… Cocktai…      0           0          0               0           1   
 5 John C… Whiskies      0           1          0               0           0   
 6 Cherry… Cocktai…      1.25        0          0               0           0   
 7 Casa B… Cocktai…      2           0          1.5             0           0   
 8 Caribb… Cocktai…      0.5         0          0               0           0   
 9 Amber … Cordial…      0           0.25       0               0           0   
10 The Jo… Whiskies      0           0.5        0               0           0   
# ℹ 927 more rows
# ℹ 35 more variables: powdered_sugar <dbl>, dark_rum <dbl>,
#   cranberry_juice <dbl>, pineapple_juice <dbl>, bourbon_whiskey <dbl>,
#   simple_syrup <dbl>, cherry_flavored_brandy <dbl>, light_cream <dbl>,
#   triple_sec <dbl>, maraschino <dbl>, amaretto <dbl>, grenadine <dbl>,
#   apple_brandy <dbl>, brandy <dbl>, gin <dbl>, anisette <dbl>,
#   dry_vermouth <dbl>, apricot_flavored_brandy <dbl>, bitters <dbl>, …

cocktails_df %>% 
  # filter for the desired cocktail
  dplyr::filter(name == 'Leap Frog Highball') %>% 
  dplyr::pull(gin)

[1] 2

Two ounces (oz) of gin are in the Leap Frog Highball.

Exercise 3

Prepare a recipes::recipe object without a target but give name and category as ‘id’ roles. Add steps to normalize the predictors and perform PCA. Finally prep the data and save it in a variable.

How many predictor variables are prepped by the recipe?

SOLUTION:

# create a recipe: y~. with an outcome/target, but here we just use ~.
pca_rec <- recipes::recipe(~., data = cocktails_df) 
pca_rec %>% summary()

# A tibble: 42 × 4
   variable        type      role      source  
   <chr>           <list>    <chr>     <chr>   
 1 name            <chr [3]> predictor original
 2 category        <chr [3]> predictor original
 3 light_rum       <chr [2]> predictor original
 4 lemon_juice     <chr [2]> predictor original
 5 lime_juice      <chr [2]> predictor original
 6 sweet_vermouth  <chr [2]> predictor original
 7 orange_juice    <chr [2]> predictor original
 8 powdered_sugar  <chr [2]> predictor original
 9 dark_rum        <chr [2]> predictor original
10 cranberry_juice <chr [2]> predictor original
# ℹ 32 more rows

pca_rec <- pca_rec %>% 
  # change the roles of name and category to 'id' from 'predictor'
  recipes::update_role(name, category, new_role = "id") %>%
  # normalize the remaining predictors
  recipes::step_normalize(all_predictors()) %>%
  # convert the predictors to principle components
  recipes::step_pca(all_predictors())

# note there are 40 predictors, but that nothing has been calculated yet
pca_rec %>% summary()

# A tibble: 42 × 4
   variable        type      role      source  
   <chr>           <list>    <chr>     <chr>   
 1 name            <chr [3]> id        original
 2 category        <chr [3]> id        original
 3 light_rum       <chr [2]> predictor original
 4 lemon_juice     <chr [2]> predictor original
 5 lime_juice      <chr [2]> predictor original
 6 sweet_vermouth  <chr [2]> predictor original
 7 orange_juice    <chr [2]> predictor original
 8 powdered_sugar  <chr [2]> predictor original
 9 dark_rum        <chr [2]> predictor original
10 cranberry_juice <chr [2]> predictor original
# ℹ 32 more rows

# calculate prepare the data per the steps in the recipe
pca_prep <- recipes::prep(pca_rec)
pca_prep %>% summary

# A tibble: 7 × 4
  variable type      role      source  
  <chr>    <list>    <chr>     <chr>   
1 name     <chr [3]> id        original
2 category <chr [3]> id        original
3 PC1      <chr [2]> predictor derived 
4 PC2      <chr [2]> predictor derived 
5 PC3      <chr [2]> predictor derived 
6 PC4      <chr [2]> predictor derived 
7 PC5      <chr [2]> predictor derived 

• There are 40 predictors and 2 id variables before the data is prepped.
• Once prepped, the PCA returns just 5 components by default, so we have (post-prep) 5 predictors and 2 id variables.

Exercise 4

Apply the recipes::tidy verb to the prepped recipe in the last exercise. The result is a table identifying the information generated and stored by each step in the recipe from the input data.

To see the values calculated for normalization, apply the recipes::tidy verb as before, but with second argument = 1.

What ingredient is the most used, on average?

SOLUTION:

# tidy returns a tibble with the calculations performed by prep
pca_prep %>% recipes::tidy()

# A tibble: 2 × 6
  number operation type      trained skip  id             
   <int> <chr>     <chr>     <lgl>   <lgl> <chr>          
1      1 step      normalize TRUE    FALSE normalize_zkFR2
2      2 step      pca       TRUE    FALSE pca_wpByh      

# if we select the first (normalization) step we get the values calculated:
# - the mean and standard deviation for each variable
foo <- pca_prep %>% recipes::tidy(1)
foo

# A tibble: 80 × 4
   terms           statistic  value id             
   <chr>           <chr>      <dbl> <chr>          
 1 light_rum       mean      0.161  normalize_zkFR2
 2 lemon_juice     mean      0.229  normalize_zkFR2
 3 lime_juice      mean      0.138  normalize_zkFR2
 4 sweet_vermouth  mean      0.0691 normalize_zkFR2
 5 orange_juice    mean      0.185  normalize_zkFR2
 6 powdered_sugar  mean      0.0891 normalize_zkFR2
 7 dark_rum        mean      0.0454 normalize_zkFR2
 8 cranberry_juice mean      0.0363 normalize_zkFR2
 9 pineapple_juice mean      0.0608 normalize_zkFR2
10 bourbon_whiskey mean      0.0768 normalize_zkFR2
# ℹ 70 more rows

# we can just filter to find the largest mean value
# first, isolate the mean values
foo %>% dplyr::filter(statistic == 'mean') %>% 
  # once we have just the mean values, filter out the row with the max value 
  dplyr::filter(value == max(value))

# A tibble: 1 × 4
  terms statistic value id             
  <chr> <chr>     <dbl> <chr>          
1 gin   mean      0.252 normalize_zkFR2

On average, it is gin that is the largest component of the cocktails, with just over 1/4 oz per cocktail.

Exercise 5

Now look at the result of the PCA, applying the recipes::tidy verb as before, but with second argument = 2. Save the result in a variable and filter for the components PC1 to PC5. Mutate the resulting component column so that the values are factors, ordering them in the order they appear using the forcats::fct_inorder verb.

Plot this data using ggplot2 and the code below

ggplot(aes(value, terms, fill = terms)) +
geom_col(show.legend = FALSE) +
facet_wrap(~component, nrow = 1) +
labs(y = NULL) +
theme(axis.text=element_text(size=7),
      axis.title=element_text(size=14,face="bold"))

How would you describe the drinks represented by PC1?

SOLUTION:

# the tidy operation shows the weights of each ingredient, 
# for each principal component.
# - i.e. PC1 (along with PC2 - PC5) is a weighted sum of ingredients
bar <- pca_prep %>% recipes::tidy(2)
bar

# A tibble: 1,600 × 4
   terms             value component id       
   <chr>             <dbl> <chr>     <chr>    
 1 light_rum        0.163  PC1       pca_wpByh
 2 lemon_juice     -0.0140 PC1       pca_wpByh
 3 lime_juice       0.224  PC1       pca_wpByh
 4 sweet_vermouth  -0.0661 PC1       pca_wpByh
 5 orange_juice     0.0308 PC1       pca_wpByh
 6 powdered_sugar  -0.476  PC1       pca_wpByh
 7 dark_rum         0.124  PC1       pca_wpByh
 8 cranberry_juice  0.0954 PC1       pca_wpByh
 9 pineapple_juice  0.119  PC1       pca_wpByh
10 bourbon_whiskey  0.0963 PC1       pca_wpByh
# ℹ 1,590 more rows

# plot to show the ingredient weights
bar %>%
  # since there are only 5 components, this is redundant
  dplyr::filter(component %in% paste0("PC", 1:5)) %>%
  # change component from a character to a factor, and give them an order
  dplyr::mutate(component = forcats::fct_inorder(component)) %>%
  # plot
  ggplot(aes(value, terms, fill = terms)) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~component, nrow = 1) +
  labs(y = NULL) +
  theme(axis.text=element_text(size=7),
        axis.title=element_text(size=14,face="bold"))

Exercise 6

As in the last exercise, use the variable with the tidied PCA data and use only PCA components PC1 to PC4. Take/slice the top 8 ingedients by component, ordered by their absolute value using the verb dplyr::slice_max. Next, generate a grouped table using gt::gt, colouring the cell backgrounds (i.e. fill) with green for values $\ge0$ and red for values $<0$.

What is the characteristic alcoholic beverage of each of the first 4 principle components.

SOLUTION:

bar %>%
  # filter our the rows for PC1 - PC4
  dplyr::filter(component %in% paste0("PC", 1:4)) %>%
  # group by component (i.e. principal component) 
  dplyr::group_by(component) %>%
  # for each group, take the top 8 ingredients by absolute value
  dplyr::slice_max(n = 8, order_by = abs(value)) %>% 
  # now make a nicely formatted table
  gt::gt() %>% 
  # make/apply a table style: this one for values < 0
  gt::tab_style(
    style = list(
      gt::cell_fill(color = "red"),
      gt::cell_text(weight = "bold")
      ),
    locations = gt::cells_body(
      columns = value,
      rows = value < 0
    )
  ) %>% 
  # make/apply another table style: this one for values >= 0
    gt::tab_style(
    style = list(
      gt::cell_fill(color = "green"),
      gt::cell_text(weight = "bold")
      ),
    locations = gt::cells_body(
      columns = value,
      rows = value >= 0
    )
  ) %>% 
  # apply a theme; any theme will do
  gtExtras::gt_theme_espn()

terms	value	id
PC1
powdered_sugar	-0.4764442	pca_wpByh
simple_syrup	0.3125978	pca_wpByh
whole_egg	-0.2903794	pca_wpByh
egg_white	-0.2643614	pca_wpByh
gin	-0.2588481	pca_wpByh
port	-0.2354010	pca_wpByh
lime_juice	0.2240295	pca_wpByh
blanco_tequila	0.2025436	pca_wpByh
PC2
dry_vermouth	0.4332412	pca_wpByh
sweet_vermouth	0.3754366	pca_wpByh
powdered_sugar	-0.3273723	pca_wpByh
lemon_juice	-0.2651836	pca_wpByh
simple_syrup	-0.2512150	pca_wpByh
gin	0.2437374	pca_wpByh
whole_egg	-0.2191547	pca_wpByh
port	-0.1763370	pca_wpByh
PC3
gin	0.3780348	pca_wpByh
egg_white	0.3094631	pca_wpByh
benedictine	-0.2864463	pca_wpByh
whole_egg	-0.2825812	pca_wpByh
blended_scotch_whiskey	-0.2352922	pca_wpByh
lemon_juice	0.2318927	pca_wpByh
vodka	-0.2167814	pca_wpByh
apricot_flavored_brandy	0.2134899	pca_wpByh
PC4
grenadine	0.4068737	pca_wpByh
orange_juice	0.3625622	pca_wpByh
vodka	0.3008406	pca_wpByh
simple_syrup	-0.2533007	pca_wpByh
half_and_half	0.2493552	pca_wpByh
egg_white	0.2465323	pca_wpByh
cranberry_juice	0.2228099	pca_wpByh
white_creme_de_cacao	0.2089063	pca_wpByh

Principal components and similar methods are very useful in reducing the complexity of our models. In this case we reduced the 40 original predictors to just 5 predictors.

The challenge with using these methods is attaching meaning to the revised predictors, and this is important when we need to explain our models. The computer can compute the new predictors but they can’t tell us what they represent. For this we need to look at the structure of the new predictors and see if we can attach some meaning to them; often the solution is to give them names that capture the underlying structure.

In this case, looking at the ingredients that make up the PCA predictors

• PC1 represents a drink with:

  • little or no sugar, egg, gin or port; some or a lot of syrup and citrus juice
  • most often / mainly tequila

• PC2 represents a drink with:

  • little or no sugar, syrup, or citrus juice
  • most often / mainly vermouth

• PC3 represents a drink with:

  • little or no egg, whiskey or vodka

  • most often / mainly gin

• PC4 represents a drink with

  • little or no syrup; some or a lot of juice and dairy product

  • most often / mainly grenadine and vodka

Exercise 7

For this exercise, bake the prepped PCA recipe using recipes::bake on the original data and plot each cocktail by its PC1, PC2 component, using

ggplot(aes(PC1, PC2, label = name)) +
  geom_point(aes(color = category), alpha = 0.7, size = 2) +
  geom_text(check_overlap = TRUE, hjust = "inward") + 
  labs(color = NULL)

Can you create an interpretation of the PCA analysis?

SOLUTION:

# bake the dataset cocktails_df, creating a new dataset
recipes::bake(pca_prep, new_data = cocktails_df) %>%
  ggplot(aes(PC1, PC2, label = name)) +
  geom_point(aes(color = category), alpha = 0.7, size = 2) +
  geom_text(check_overlap = TRUE, hjust = "inward") + 
  labs(color = NULL)

In this exercise we are plotting the cocktails against the first two principal components and trying to interpret the results.

The interpretation is more difficult now than it was in the last exercise where we found an interpretation for each principal component. Now we are looking at each cocktail in terms of combinations of PC1 and PC2, both positive and negative.

It appears that the lower left quadrant (negative PC1 and PC2 values) are mainly cocktail classics. If you look at the components of PC1 and PC2 from the last exercise and negate them, you can describe the cocktails in this quadrant: they have egg /egg-white, port, sugar, no vermouth or gin or tequila.

The lower right quadrant is positive PC1 and negative PC2. PC1 contains juice and syrup, while PC2 is negative juice and syrup - so in this quadrant we have cocktails that are like PC1 (juice and syrup) and unlike PC2 (juice and syrup & sugar). The cocktails in this quadrant have citrus juice, are sweet from the use of syrup & sugar and likely have tequila.

The top half of the plot shows cocktails clustered along the PC1=0 axis, so those are mainly tequila cocktails, very much like PC2.

Is there an opportunity to create a new cocktail in the upper left or upper right quadrants?

Grading

Total points available: 30 points.

Component	Points
Ex 1 - 7	30